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Elementary (PK5)
Research synthesis is AI-generated, human reviewed. Updated 09/2025.
Displaying 211 - 240 of 275
Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming
Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla. (05/2024). arXiv. http://arxiv.org/pdf/2310.10690v3
Large Language Models for Education: A Survey
Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu and Philip S. Yu. (05/2024). arXiv. http://arxiv.org/pdf/2405.13001v1
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Kun Zhou, Beichen Zhang, Jiapeng Wang, Zhipeng Chen, Wayne Xin Zhao, Jing Sha, Zhichao Sheng, Shijin Wang, Ji-Rong Wen. (05/2024). arXiv. http://arxiv.org/pdf/2405.14365v1
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
Gyeong-Geon Lee, Xiaoming Zhai. (05/2024). arXiv. https://arxiv.org/pdf/2405.07163
Rethinking the ‘A’ in STEAM: Insights from and for AI Literacy Education
Pekka Mertala, Janne Fagerlund, Tomi Slotte Dufva. (05/2024). arXiv. https://arxiv.org/pdf/2405.18179
Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education
Owen Henkel, Libby Hills, Adam Boxer, Bill Roberts, Zach Levonian. (05/2024). ACM Digital Library. https://dl.acm.org/doi/pdf/10.1145/3657604.3664693
Pros and Cons of Artificial Intelligence-ChatGPT Adoption in Education Settings: A Literature Review and Future Research Agendas
Idria Maita, Saide Saide, Afifah Mesha Putri, Didi Muwardi. (04/2024). IEEE. https://ieeexplore.ieee.org/document/10510580
AI AND PERSONALIZED LEARNING: BRIDGING THE GAP WITH MODERN EDUCATIONAL GOALS
Kristjan-Julius Laak, Jaan Aru. (04/2024). arXiv. https://arxiv.org/pdf/2404.02798
Large Language Models for Education: A Survey and Outlook
Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen. (04/2024). arXiv. http://arxiv.org/pdf/2403.18105v2
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, Yong Yu. (04/2024). arXiv. http://arxiv.org/pdf/2401.08664v3
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini. (04/2024). arXiv. http://arxiv.org/pdf/2404.06186v1
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes
Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, Dorottya Demszky. (04/2024). arXiv. http://arxiv.org/pdf/2310.10648v3
Chatbot dialogic reading boosts comprehension for Chinese kindergarteners with higher language skills
Xi Cheng, Li Yin, Chaochao Lin, Zhaoning Shi, Hanxiao Zheng, Leqi Zhu, Xiabi Liu, Keran Chen, Rui Dong. (04/2024). ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S0022096523002187
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions
Richard Tong, Haoyang Li, Joleen Liang, Qingsong Wen. (03/2024). arXiv. http://arxiv.org/pdf/2403.14689v2
AGI: Artificial General Intelligence for Education
Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu, Guoyu Lu, Sheng Li, Tianming Liu and Xiaoming Zhai. (03/2024). arXiv. http://arxiv.org/pdf/2304.12479v5
Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform
Dorottya Demszky, Rose E. Wang, Sean Geraghty, Carol Yu. (03/2024). ACM Digital Library. https://dl.acm.org/doi/10.1145/3636555.3636924
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
Zewei Tian, Min Sun, Alex Liu, Shawon Sarkar, Jing Liu. (03/2024). arXiv. http://arxiv.org/pdf/2403.03920v1
Are LLMs Useful in the Poorest Schools? TheTeacher.AI in Sierra Leone
Jun Ho Choi, Oliver Garrod, Paul Atherton, Andrew Joyce-Gibbons, Daniel Bj¬örkegren, Miriam Mason-Sesay. (02/2024). arXiv. http://arxiv.org/pdf/2310.02982v2
Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling
Chao Zhang, Xuechen Liu, Katherine Ziska, Soobin Jeon, Chi-Lin Yu, Ying Xu. (02/2024). arXiv. http://arxiv.org/pdf/2402.01927v2
The Impact of Artificial Intelligence on Students' Learning Experience
Abill Robert, Kaledio Potter, Louis Frank. (02/2024). SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4716747
Are Lesson Plans Created by ChatGPT More Effective? An Experimental Study
Muhammet Remzi Karaman, Idris G¬öksu. (02/2024). International Journal of Technology in Education. https://www.researchgate.net/publication/377964050_Are_Lesson_Plans_Created_by_…
Edu-ConvoKit An Open-Source Library for Education Conversation Data
Rose E. Wang, Dorottya Demszky. (02/2024). arXiv. http://arxiv.org/pdf/2402.05111v1
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
Paul Denny, Sumit Gulwani, Neil T. Heffernan, Tanja KŠser, Steven Moore, Anna N. Rafferty, Adish Singla. (02/2024). arXiv. https://arxiv.org/pdf/2402.01580
What Explains Teachers' Trust of AI in Education across Six Countries?
Olga Viberg, Mutlu Cukurova, Yael Feldman-Maggor, Giora Alexandron, Shizuka Shirai, Susumu Kanemune, Barbara Wasson, Cathrine Tomte, Daniel Spikol, Marcelo Milrad, Raquel Coelho, RenŽ F. Kizilcec. (02/2024). arXiv. https://arxiv.org/pdf/2312.01627
Foundation Models in Augmentative and Alternative Communication: Opportunities and Challenges
Ambra Di Paola, Serena Muraro, Roberto Marinelli, Christian Pilato. (01/2024). arXiv. http://arxiv.org/pdf/2401.08866v1
Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning
Gyeong-Geon Lee, Xiaoming Zhai. (01/2024). arXiv. http://arxiv.org/pdf/2402.01674v1
Generative AI and Its Educational Implications
Kacper Lodzikowski, Peter W. Foltz, John T. Behrens. (01/2024). arXiv. https://arxiv.org/pdf/2401.08659
Can Generative AI And ChatGPT Outperform Humans On Cognitive-Demanding Problem-Solving Tasks In Science?
Xiaoming Zhai, Matthew Nyaaba, Wenchao Ma. (01/2024). arXiv. http://arxiv.org/pdf/2401.15081v1
